Financial_Analysis---Python-Pandas/Financial Analysis.py at main · LauraFlorio/Financial_Analysis---Python-Pandas · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
#!/usr/bin/env python
# coding: utf-8

# In[1]:


# dataset source: https://www.kaggle.com/datasets/nitindatta/finance-data?select=Finance_data.csv


# In[2]:


# !pip install numpy


# In[3]:


import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


# In[4]:


sns.set_style("darkgrid")


# In[5]:


df = pd.read_csv("D:\Mente\Programacao\Python\Financial Analysis\Projeto1\Finance_data.csv")


# In[6]:


df.head()


# In[7]:


condicoes = [(df['Mutual_Funds'] == 1),
             (df['Equity_Market'] == 1),
             (df['Debentures'] == 1),
             (df['Government_Bonds'] == 1),
             (df['Fixed_Deposits'] == 1),
             (df['PPF'] == 1),
             (df['Gold'] == 1)]
opcoes = ['Mutual_Funds', 'Equity_Market', 'Debentures', 'Government_Bonds', 'Fixed_Deposits', 'PPF', 'Gold']
df['Preferred_Inv_Option'] = np.select(condicoes, opcoes)
df


# In[8]:


data = df["gender"].value_counts()
colors = sns.color_palette("Set3")
plt.pie(data, labels=data.index, autopct='%.0f%%', colors=colors, shadow=True, explode=[0.05,0.05])
plt.title("GENDER OF THE PARTICIPANTS", fontsize=10)
plt.show()


# In[9]:


graph2 = sns.countplot(x="age", data=df, palette="Set2", linewidth=1, edgecolor="black")
for label in graph2.containers:
    graph2.bar_label(label, padding=3)
graph2.set(ylabel=None,xlabel=None,yticklabels=[])
plt.title("AGE OF THE PARTICIPANTS", fontsize=10)
plt.show()


# In[10]:


plt.figure(figsize=(17,7))
graph3=plt.subplot(1,2,1)
sns.countplot(x=df["gender"], hue=df["Investment_Avenues"], palette="summer", linewidth=3, edgecolor="white")
for label in graph3.containers:
    graph3.bar_label(label, size=13, padding=2)
graph3.set(ylabel=None,xlabel=None,yticklabels=[])
graph3.xaxis.set_tick_params(labelsize=12)
plt.legend(fontsize=14)
plt.title("INVESTMENT AVENUE", fontsize=12)

graph4=plt.subplot(1,2,2)
sns.countplot(x=df['gender'],hue=df["Stock_Marktet"], palette="hot", linewidth=3, edgecolor="white")
for label in graph4.containers:
    graph4.bar_label(label, size=13, padding=2)
graph4.set(ylabel=None,xlabel=None,yticklabels=[])
graph4.xaxis.set_tick_params(labelsize=12)
plt.legend(fontsize=14)
plt.title("STOCK MARKET", fontsize=12)

plt.show()


# In[11]:


graph5=sns.countplot(x=df["Factor"], palette="coolwarm", linewidth=2, edgecolor="black")
for label in graph5.containers:
    graph5.bar_label(label)
graph5.set(ylabel=None,xlabel=None,yticklabels=[])
plt.title("FACTOR CONSIDERED FOR INVESTING", fontsize=12)
plt.show()


# In[12]:


plt.figure(figsize=(10,5))
graph6=sns.pointplot(x="Purpose", y="age", data=df, linestyles="--", capsize=.3, color="Blue")
plt.title("INVESTMENT OBJECTIVE")
graph6.set(xlabel=None)
graph6.set_ylabel("Age",size=10)
graph6.xaxis.set_tick_params(labelsize=10)
graph6.yaxis.set_tick_params(labelsize=10)
plt.show()


# In[13]:


plt.figure(figsize=(15,5))
graph7=plt.subplot(1,2,1)
sns.countplot(y=df["Duration"], hue=df["gender"], palette="viridis", linewidth=2, edgecolor="black")
for label in graph7.containers:
    graph7.bar_label(label, size=12, padding=3)
graph7.set(ylabel=None,xlabel=None,xticklabels=[])
graph7.yaxis.set_tick_params(labelsize=13)
plt.legend(fontsize=12)
plt.title("DURATION", fontsize=15)

plt.subplot(1,2,2)
graph8=sns.countplot(y=df["Invest_Monitor"], hue=df["gender"], palette="seismic", linewidth=2, edgecolor="black")
for label in graph8.containers:
    graph8.bar_label(label, size=12, padding=3)
graph8.set(ylabel=None,xlabel=None,xticklabels=[])
graph8.yaxis.set_tick_params(labelsize=13)
plt.legend(fontsize=12)
plt.title("MONITORIZATION FREQUENCY", fontsize=15)

plt.show()


# In[14]:


plt.figure(figsize=(9,5))
graph9 = sns.countplot(y="Preferred_Inv_Option", data=df, palette="Set2", linewidth=1, edgecolor="black",
                      order = df['Preferred_Inv_Option'].value_counts().index)
for label in graph9.containers:
    graph9.bar_label(label, padding=3)
graph9.set(ylabel=None,xlabel=None,xticklabels=[])
graph9.xaxis.set_tick_params(labelsize=10)
plt.title("INVESTMENT OPTIONS")
plt.show()


# In[15]:


plt.figure(figsize=(18,8))
graph10=sns.countplot(x=df["Preferred_Inv_Option"], hue=df["gender"], palette="viridis", linewidth=2, edgecolor="black")
for label in graph10.containers:
    graph10.bar_label(label, size=12, padding=3)
graph10.set(ylabel=None,xlabel=None,yticklabels=[])
graph10.xaxis.set_tick_params(labelsize=13)
plt.title("FAVOURITE INVESTMENT OPTION")
plt.legend(fontsize=12)
plt.show()


# In[ ]: